CN116853073B - New energy electric automobile energy management method and system - Google Patents
New energy electric automobile energy management method and system Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 71
- 238000011084 recovery Methods 0.000 claims abstract description 58
- 238000012544 monitoring process Methods 0.000 claims abstract description 37
- 238000005457 optimization Methods 0.000 claims abstract description 34
- 238000012549 training Methods 0.000 claims abstract description 30
- 238000013528 artificial neural network Methods 0.000 claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims description 32
- 238000012545 processing Methods 0.000 claims description 32
- 230000005611 electricity Effects 0.000 claims description 22
- 238000004891 communication Methods 0.000 claims description 12
- 238000010606 normalization Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 238000007499 fusion processing Methods 0.000 claims description 6
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L1/00—Supplying electric power to auxiliary equipment of vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L7/00—Electrodynamic brake systems for vehicles in general
- B60L7/10—Dynamic electric regenerative braking
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/10—Vehicle control parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/42—Drive Train control parameters related to electric machines
- B60L2240/423—Torque
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
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Abstract
The invention provides a new energy electric automobile energy management method and system, wherein the method comprises the following steps: acquiring a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module in real time; generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model; obtaining output torque of a driving motor, and determining corresponding optimization targets and constraint conditions in a battery module, an electric load module and a braking energy recovery module according to the output torque through an energy management model; and generating a corresponding power distribution strategy according to the optimization target and the constraint condition so as to complete corresponding control. The invention can effectively improve the efficiency of energy management and correspondingly improve the use experience of users.
Description
Technical Field
The invention relates to the technical field of new energy electric vehicles, in particular to a new energy electric vehicle energy management method and system.
Background
Along with the progress of technology and the rapid development of productivity, new energy electric automobile technology has also been rapidly developed, has gradually gained acceptance by people, has been popularized in people's daily life, and has greatly facilitated people's life.
Most of the existing automobile manufacturers can set an energy management system in the new energy electric automobile for monitoring and controlling electric energy output by the power battery pack in real time, kinetic energy recovered by the braking energy recovery system in real time and the like, so that coordination and optimization among different energies are realized, and reliability of the new energy electric automobile is improved.
In the prior art, a plurality of different control strategies or control rules are arranged in an energy management system, and the control of the energy in the vehicle is completed by carrying out different strategies or rules in real time in the running process of the new energy electric vehicle, however, the control mode cannot accurately adapt to the complex and changeable running working conditions of the new energy electric vehicle, so that certain control deviation can occur, the use performance of the vehicle is reduced, and the use experience of electricity is correspondingly reduced.
Disclosure of Invention
Based on the above, the invention aims to provide a new energy electric automobile energy management method and system, which are used for solving the problem that the use performance of a vehicle is reduced due to certain control deviation possibly occurring in the prior art.
The first aspect of the embodiment of the invention provides:
a new energy electric vehicle energy management method, wherein the method comprises:
communication connection with a battery module, an electric load module and a braking energy recovery module in a vehicle is respectively established through a CAN bus, and a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module are obtained in real time;
generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model;
obtaining output torque of a driving motor, and determining corresponding optimization targets and constraint conditions in the battery module, the electricity load module and the braking energy recovery module according to the output torque through the energy management model;
and generating a corresponding power distribution strategy according to the optimization target and the constraint condition, and completing control of actual working power of the battery module, the electricity load module and the braking energy recovery module according to the power distribution strategy.
The beneficial effects of the invention are as follows: the state parameters of the battery module, the electricity load module and the brake energy recovery module are obtained in real time, so that the corresponding real-time working state can be known in real time, a corresponding monitoring data set is generated based on the state parameters, and a preset neural network is trained in real time, so that the current neural network can be adapted to the battery module, the electricity load module and the brake energy recovery module in the current vehicle, a required energy management model is generated, further, a required power distribution strategy can be generated in real time through the energy management model, and finally, the whole vehicle controller can control the actual working power of the modules according to the power distribution strategy, the service performance of the vehicle is correspondingly improved, meanwhile, control deviation is not generated, and the use experience of a user is improved.
Further, the step of generating the corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter includes:
extracting a first subset, a second subset and a third subset which correspond to the first state parameter, the second state parameter and the third state parameter respectively based on preset weights, and carrying out normalization processing on the first subset, the second subset and the third subset;
fusion processing is carried out on the first subset, the second subset and the third subset after normalization processing to generate a corresponding target data set, and serialization processing is carried out on the target data set through a DTW algorithm to generate a plurality of corresponding feature sequences;
integrating a plurality of the characteristic sequences to correspondingly generate the monitoring data set.
Further, the step of training the preset neural network through the monitoring data set to generate a corresponding energy management model includes:
extracting a plurality of characteristic sequences contained in the monitoring data set, and sequentially inputting the plurality of characteristic sequences into a coding layer, an analysis layer and an output layer in the preset neural network;
and carrying out self-adaptive training on original parameters in the coding layer, the analysis layer and the output layer through a plurality of characteristic sequences so as to correspondingly generate the energy management model.
Further, the step of adaptively training the original parameters in the coding layer, the parsing layer and the output layer through the plurality of feature sequences to correspondingly generate the energy management model includes:
sequentially extracting a plurality of characteristic values contained in the characteristic sequences, and inputting the characteristic values into a transducer encoder in the encoding layer one by one;
performing multi-task learning processing on the transducer encoder through a plurality of characteristic values so as to train a first original parameter in the transducer encoder into a first target parameter, and inputting the first target parameter into the analysis layer;
and performing meta learning processing on the analysis layer through the first target parameters so as to train the second original parameters in the analysis layer into corresponding second target parameters, thereby correspondingly generating the energy management model.
Further, the step of generating a corresponding power allocation policy according to the optimization objective and the constraint condition includes:
respectively acquiring a first rated power of the battery module, a second rated power of the electric load module and a third rated power of the braking energy recovery module;
a power ratio between the first power rating and the second and third power ratings is calculated, and the power allocation strategy is generated from the optimization objective and the constraints based on the power ratio.
Further, the step of generating the power allocation policy according to the optimization objective and the constraint condition based on the power ratio includes:
extracting a first ratio corresponding to the battery module, a second ratio corresponding to the electric load module and a third ratio corresponding to the braking energy recovery module from the power ratios, and generating corresponding first, second and third weights according to the first, second and third ratios based on the constraint conditions;
and respectively adjusting the weight values respectively contained in the first weight, the second weight and the third weight according to the optimization target so as to correspondingly generate the power distribution strategy according to the weight values.
Further, the method further comprises:
and establishing communication connection with an instrument panel in the vehicle currently through the CAN bus, and displaying the real-time working power of the battery module, the electricity load module and the braking energy recovery module in the instrument panel in real time.
A second aspect of an embodiment of the present invention proposes:
a new energy electric vehicle energy management system, wherein the system comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for respectively establishing communication connection with a battery module, an electric load module and a braking energy recovery module in a vehicle through a CAN bus, and acquiring a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module in real time;
the training module is used for generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model;
the processing module is used for acquiring the output torque of the driving motor and determining a corresponding optimization target and constraint conditions in the battery module, the electricity load module and the braking energy recovery module according to the output torque through the energy management model;
and the control module is used for generating a corresponding power distribution strategy according to the optimization target and the constraint condition, and controlling the actual working power of the battery module, the electricity load module and the braking energy recovery module according to the power distribution strategy.
Further, the training module is specifically configured to:
extracting a first subset, a second subset and a third subset which correspond to the first state parameter, the second state parameter and the third state parameter respectively based on preset weights, and carrying out normalization processing on the first subset, the second subset and the third subset;
fusion processing is carried out on the first subset, the second subset and the third subset after normalization processing to generate a corresponding target data set, and serialization processing is carried out on the target data set through a DTW algorithm to generate a plurality of corresponding feature sequences;
integrating a plurality of the characteristic sequences to correspondingly generate the monitoring data set.
Further, the training module is specifically configured to:
extracting a plurality of characteristic sequences contained in the monitoring data set, and sequentially inputting the plurality of characteristic sequences into a coding layer, an analysis layer and an output layer in the preset neural network;
and carrying out self-adaptive training on original parameters in the coding layer, the analysis layer and the output layer through a plurality of characteristic sequences so as to correspondingly generate the energy management model.
Further, the training module is specifically configured to:
sequentially extracting a plurality of characteristic values contained in the characteristic sequences, and inputting the characteristic values into a transducer encoder in the encoding layer one by one;
performing multi-task learning processing on the transducer encoder through a plurality of characteristic values so as to train a first original parameter in the transducer encoder into a first target parameter, and inputting the first target parameter into the analysis layer;
and performing meta learning processing on the analysis layer through the first target parameters so as to train the second original parameters in the analysis layer into corresponding second target parameters, thereby correspondingly generating the energy management model.
Further, the control module is specifically configured to:
respectively acquiring a first rated power of the battery module, a second rated power of the electric load module and a third rated power of the braking energy recovery module;
a power ratio between the first power rating and the second and third power ratings is calculated, and the power allocation strategy is generated from the optimization objective and the constraints based on the power ratio.
Further, the control module is specifically further configured to:
extracting a first ratio corresponding to the battery module, a second ratio corresponding to the electric load module and a third ratio corresponding to the braking energy recovery module from the power ratios, and generating corresponding first, second and third weights according to the first, second and third ratios based on the constraint conditions;
and respectively adjusting the weight values respectively contained in the first weight, the second weight and the third weight according to the optimization target so as to correspondingly generate the power distribution strategy according to the weight values.
Further, the new energy electric automobile energy management system further comprises a display module, wherein the display module is specifically used for:
and establishing communication connection with an instrument panel in the vehicle currently through the CAN bus, and displaying the real-time working power of the battery module, the electricity load module and the braking energy recovery module in the instrument panel in real time.
A third aspect of an embodiment of the present invention proposes:
a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the new energy electric vehicle energy management method as described above when executing the computer program.
A fourth aspect of the embodiment of the present invention proposes:
a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the new energy electric vehicle energy management method as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of a new energy electric vehicle energy management method according to a first embodiment of the present invention;
fig. 2 is a block diagram of a new energy electric vehicle energy management system according to a sixth embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a method for managing energy of a new energy electric vehicle according to a first embodiment of the present invention is shown, where the method for managing energy of a new energy electric vehicle according to the first embodiment of the present invention can control actual working power of each module according to a power allocation policy generated in real time, so that the service performance of the vehicle is correspondingly improved, and meanwhile, no control deviation is generated, thereby improving the use experience of a user.
Specifically, the new energy electric automobile energy management method provided by the embodiment specifically includes the following steps:
step S10, respectively establishing communication connection with a battery module, an electric load module and a braking energy recovery module in a vehicle through a CAN bus, and acquiring a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module in real time;
step S20, generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model;
step S30, obtaining output torque of a driving motor, and determining corresponding optimization targets and constraint conditions in the battery module, the electricity load module and the braking energy recovery module according to the output torque through the energy management model;
and S40, generating a corresponding power distribution strategy according to the optimization target and the constraint condition, and completing control of actual working power of the battery module, the electricity load module and the braking energy recovery module according to the power distribution strategy.
Specifically, in this embodiment, it is first to be noted that the new energy electric vehicle energy management method can be applied to new energy electric vehicles of different models, and is used to improve the management efficiency of the internal energy of the new energy electric vehicle, and correspondingly improve the service performance of the new energy electric vehicle. The energy recovery system comprises a battery module, an electric load module and a braking energy recovery module, wherein the battery module is a power battery pack, the electric load module is a driving motor, an air conditioner, a car lamp and other electric appliances in the vehicle, and the braking energy recovery module is a kinetic energy recovery system. Further, in order to facilitate the energy control, it is necessary to first acquire the first state parameter of the battery module, the second state parameter of the electric load module, and the third state parameter of the braking energy recovery module. Specifically, the state parameters may include parameters such as voltage, current, and power.
Further, the obtained state parameters can be further integrated into a corresponding monitoring data set, and specifically, the monitoring data set can be used for carrying out corresponding training on a preset neural network so as to adjust parameters in the current neural network in real time and generate a corresponding energy management model. Preferably, the preset neural network is set as a CNN neural network. Based on the above, the output torque of the driving motor obtained in real time is input into the current energy management model, so that the current energy management model outputs the corresponding optimization target and constraint conditions, and a power distribution strategy capable of managing the battery module, the electric load module and the braking energy recovery module can be further generated.
Second embodiment
Specifically, in this embodiment, it should be noted that the step of generating the corresponding monitoring data set according to the first state parameter, the second state parameter, and the third state parameter includes:
extracting a first subset, a second subset and a third subset which correspond to the first state parameter, the second state parameter and the third state parameter respectively based on preset weights, and carrying out normalization processing on the first subset, the second subset and the third subset;
fusion processing is carried out on the first subset, the second subset and the third subset after normalization processing to generate a corresponding target data set, and serialization processing is carried out on the target data set through a DTW algorithm to generate a plurality of corresponding feature sequences;
integrating a plurality of the characteristic sequences to correspondingly generate the monitoring data set.
Specifically, in this embodiment, it should be noted that, because the importance of the battery module, the electric load module, and the braking energy recovery module in the vehicle is different, the corresponding control weights are also different, preferably, the weight of the battery module is set to 60%, the weight of the electric load module is set to 20%, and the weight of the braking energy recovery module is set to 20%, and further, the required first subset, second subset, and third subset are extracted from the corresponding state parameters according to the current weights respectively.
Further, in order to facilitate subsequent processing, the current first subset, the second subset and the third subset are normalized, and then are subjected to corresponding fusion processing, so that the three subsets can be converted into unified data and fused into a required target data set, based on the unified data, the current target data set is subjected to serialization processing through the existing DTW algorithm to generate a plurality of corresponding feature sequences, and the current feature sequences are integrated together to generate the monitoring data set, so that subsequent processing is facilitated.
Specifically, in this embodiment, it should also be noted that the step of training the preset neural network through the monitoring data set to generate the corresponding energy management model includes:
extracting a plurality of characteristic sequences contained in the monitoring data set, and sequentially inputting the plurality of characteristic sequences into a coding layer, an analysis layer and an output layer in the preset neural network;
and carrying out self-adaptive training on original parameters in the coding layer, the analysis layer and the output layer through a plurality of characteristic sequences so as to correspondingly generate the energy management model.
Specifically, in this embodiment, it should be further noted that, after the required monitoring data set is obtained in the above manner, a plurality of feature sequences contained in the monitoring data set are correspondingly extracted at this time, and meanwhile, the current plurality of feature sequences are input into an encoding layer, an analysis layer and an output layer in the preset neural network, where the feature sequences are computer codes that can be identified by the preset neural network.
Furthermore, the coding layer, the analysis layer and the output layer are sequentially and adaptively trained through the current feature sequences, so that the energy management model can be further generated.
In addition, in this embodiment, it should be noted that, the step of adaptively training original parameters in the coding layer, the parsing layer and the output layer through the plurality of feature sequences to correspondingly generate the energy management model includes:
sequentially extracting a plurality of characteristic values contained in the characteristic sequences, and inputting the characteristic values into a transducer encoder in the encoding layer one by one;
performing multi-task learning processing on the transducer encoder through a plurality of characteristic values so as to train a first original parameter in the transducer encoder into a first target parameter, and inputting the first target parameter into the analysis layer;
and performing meta learning processing on the analysis layer through the first target parameters so as to train the second original parameters in the analysis layer into corresponding second target parameters, thereby correspondingly generating the energy management model.
In addition, in this embodiment, after extracting the required several feature sequences through the above steps, the feature values included in the current feature sequence are further extracted, and at the same time, the feature values are input into the transform encoder in the coding layer, so that the current transform encoder can be subjected to a multi-task learning process, and the first original parameters in the current transform encoder can be gradually trained into the required first target parameters, and further, the first target parameters are input into the analysis layer, so that the current analysis layer performs analysis training on the current first target parameters, that is, performs the meta learning process, and at the same time, the second original parameters in the current analysis layer can be trained into the required second target parameters, thereby completing training of the coding layer and the analysis layer, that is, correspondingly generating the energy management model.
Third embodiment
In addition, in this embodiment, it should be further noted that the step of generating the corresponding power allocation policy according to the optimization target and the constraint condition includes:
respectively acquiring a first rated power of the battery module, a second rated power of the electric load module and a third rated power of the braking energy recovery module;
a power ratio between the first power rating and the second and third power ratings is calculated, and the power allocation strategy is generated from the optimization objective and the constraints based on the power ratio.
In this embodiment, in order to accurately generate the required power distribution policy, it is necessary to obtain the first rated power, the second rated power, and the third rated power of the battery module, the electric load module, and the braking energy recovery module at the time of shipment, and simultaneously calculate the power ratio between the three, and further, to integrate the current power ratio, the optimization target, and the constraint condition, so that the power distribution policy can be correspondingly generated.
Fourth embodiment
Wherein, in this embodiment, it should be noted that the step of generating the power allocation policy according to the optimization objective and the constraint condition based on the power ratio includes:
extracting a first ratio corresponding to the battery module, a second ratio corresponding to the electric load module and a third ratio corresponding to the braking energy recovery module from the power ratios, and generating corresponding first, second and third weights according to the first, second and third ratios based on the constraint conditions;
and respectively adjusting the weight values respectively contained in the first weight, the second weight and the third weight according to the optimization target so as to correspondingly generate the power distribution strategy according to the weight values.
In this embodiment, it should be noted that, after the required power ratio is obtained through the above steps, the first ratio, the second ratio and the third ratio respectively included in the current power ratio are extracted, where the first ratio corresponds to the battery module, the second ratio corresponds to the application electric load module, and the third ratio corresponds to the braking energy recovery module, based on this, for example, the obtained constraint condition is "the driving motor needs to output high torque", so that the ratio of the first ratio of the battery module needs to be lifted, the ratio of the second ratio of the electric load module needs to be reduced, and the ratio of the third ratio of the braking energy recovery module needs to be reduced, and the sum of the first weight, the second weight and the third weight is 1. Further, if the obtained optimization target is "brake energy recovery module", the magnitude of the weight value of the third weight needs to be further increased, and correspondingly, the magnitude of the weight value corresponding to the first weight and the second weight is reduced, based on which the power distribution strategy can be finally generated, so that subsequent processing is facilitated.
Fifth embodiment
In this embodiment, it should be noted that, the method further includes:
and establishing communication connection with an instrument panel in the vehicle currently through the CAN bus, and displaying the real-time working power of the battery module, the electricity load module and the braking energy recovery module in the instrument panel in real time.
In this embodiment, it should be noted that, in order to enable the driver to observe the working states of the battery module, the electricity load module and the braking energy recovery module in real time during driving the automobile, a wired communication connection with the instrument panel is further established.
Further, the working states corresponding to the current battery module, the electricity load module and the braking energy recovery module are displayed in the instrument panel in real time, so that the use experience of a user is correspondingly improved.
Referring to fig. 2, a sixth embodiment of the present invention provides:
a new energy electric vehicle energy management system, wherein the system comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for respectively establishing communication connection with a battery module, an electric load module and a braking energy recovery module in a vehicle through a CAN bus, and acquiring a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module in real time;
the training module is used for generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model;
the processing module is used for acquiring the output torque of the driving motor and determining a corresponding optimization target and constraint conditions in the battery module, the electricity load module and the braking energy recovery module according to the output torque through the energy management model;
and the control module is used for generating a corresponding power distribution strategy according to the optimization target and the constraint condition, and controlling the actual working power of the battery module, the electricity load module and the braking energy recovery module according to the power distribution strategy.
In the new energy electric automobile energy management system, the training module is specifically configured to:
extracting a first subset, a second subset and a third subset which correspond to the first state parameter, the second state parameter and the third state parameter respectively based on preset weights, and carrying out normalization processing on the first subset, the second subset and the third subset;
fusion processing is carried out on the first subset, the second subset and the third subset after normalization processing to generate a corresponding target data set, and serialization processing is carried out on the target data set through a DTW algorithm to generate a plurality of corresponding feature sequences;
integrating a plurality of the characteristic sequences to correspondingly generate the monitoring data set.
In the new energy electric automobile energy management system, the training module is specifically configured to:
extracting a plurality of characteristic sequences contained in the monitoring data set, and sequentially inputting the plurality of characteristic sequences into a coding layer, an analysis layer and an output layer in the preset neural network;
and carrying out self-adaptive training on original parameters in the coding layer, the analysis layer and the output layer through a plurality of characteristic sequences so as to correspondingly generate the energy management model.
In the new energy electric automobile energy management system, the training module is further specifically configured to:
sequentially extracting a plurality of characteristic values contained in the characteristic sequences, and inputting the characteristic values into a transducer encoder in the encoding layer one by one;
performing multi-task learning processing on the transducer encoder through a plurality of characteristic values so as to train a first original parameter in the transducer encoder into a first target parameter, and inputting the first target parameter into the analysis layer;
and performing meta learning processing on the analysis layer through the first target parameters so as to train the second original parameters in the analysis layer into corresponding second target parameters, thereby correspondingly generating the energy management model.
In the new energy electric automobile energy management system, the control module is specifically configured to:
respectively acquiring a first rated power of the battery module, a second rated power of the electric load module and a third rated power of the braking energy recovery module;
a power ratio between the first power rating and the second and third power ratings is calculated, and the power allocation strategy is generated from the optimization objective and the constraints based on the power ratio.
In the new energy electric automobile energy management system, the control module is further specifically configured to:
extracting a first ratio corresponding to the battery module, a second ratio corresponding to the electric load module and a third ratio corresponding to the braking energy recovery module from the power ratios, and generating corresponding first, second and third weights according to the first, second and third ratios based on the constraint conditions;
and respectively adjusting the weight values respectively contained in the first weight, the second weight and the third weight according to the optimization target so as to correspondingly generate the power distribution strategy according to the weight values.
Among the above-mentioned new energy electric automobile energy management system, new energy electric automobile energy management system still includes display module, display module specifically is used for:
and establishing communication connection with an instrument panel in the vehicle currently through the CAN bus, and displaying the real-time working power of the battery module, the electricity load module and the braking energy recovery module in the instrument panel in real time.
A seventh embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the new energy electric vehicle energy management method provided in the above embodiment when executing the computer program.
An eighth embodiment of the present invention provides a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the new energy electric vehicle energy management method provided in the above embodiment.
In summary, the method and the system for managing the energy of the new energy electric vehicle provided by the embodiment of the invention can complete the control of the actual working power of the module according to the power distribution strategy generated in real time, correspondingly improve the service performance of the vehicle, and simultaneously avoid control deviation, thereby improving the use experience of users.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (5)
1. The new energy electric automobile energy management method is characterized by comprising the following steps:
communication connection with a battery module, an electric load module and a braking energy recovery module in a vehicle is respectively established through a CAN bus, and a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module are obtained in real time;
generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model;
obtaining output torque of a driving motor, and determining corresponding optimization targets and constraint conditions in the battery module, the electricity load module and the braking energy recovery module according to the output torque through the energy management model;
generating a corresponding power distribution strategy according to the optimization target and the constraint condition, and completing control of actual working power of the battery module, the electricity load module and the braking energy recovery module according to the power distribution strategy;
the step of generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter comprises the following steps:
extracting a first subset, a second subset and a third subset which correspond to the first state parameter, the second state parameter and the third state parameter respectively based on preset weights, and carrying out normalization processing on the first subset, the second subset and the third subset;
fusion processing is carried out on the first subset, the second subset and the third subset after normalization processing to generate a corresponding target data set, and serialization processing is carried out on the target data set through a DTW algorithm to generate a plurality of corresponding feature sequences;
integrating a plurality of the feature sequences to correspondingly generate the monitoring data set;
the step of training a preset neural network through the monitoring data set to generate a corresponding energy management model comprises the following steps:
extracting a plurality of characteristic sequences contained in the monitoring data set, and sequentially inputting the plurality of characteristic sequences into a coding layer, an analysis layer and an output layer in the preset neural network;
performing self-adaptive training on original parameters in the coding layer, the analysis layer and the output layer through a plurality of characteristic sequences so as to correspondingly generate the energy management model;
the step of adaptively training original parameters in the coding layer, the parsing layer and the output layer through a plurality of feature sequences to correspondingly generate the energy management model includes:
sequentially extracting a plurality of characteristic values contained in the characteristic sequences, and inputting the characteristic values into a transducer encoder in the encoding layer one by one;
performing multi-task learning processing on the transducer encoder through a plurality of characteristic values so as to train a first original parameter in the transducer encoder into a first target parameter, and inputting the first target parameter into the analysis layer;
performing meta learning processing on the analysis layer through the first target parameters so as to train second original parameters in the analysis layer into corresponding second target parameters, and correspondingly generating the energy management model;
the step of generating a corresponding power allocation strategy according to the optimization target and the constraint condition comprises the following steps:
respectively acquiring a first rated power of the battery module, a second rated power of the electric load module and a third rated power of the braking energy recovery module;
calculating a power ratio between the first rated power and the second rated power and the third rated power, and generating the power distribution strategy according to the optimization target and the constraint condition based on the power ratio;
the step of generating the power allocation strategy according to the optimization objective and the constraint condition based on the power ratio comprises:
extracting a first ratio corresponding to the battery module, a second ratio corresponding to the electric load module and a third ratio corresponding to the braking energy recovery module from the power ratios, and generating corresponding first, second and third weights according to the first, second and third ratios based on the constraint conditions;
and respectively adjusting the weight values respectively contained in the first weight, the second weight and the third weight according to the optimization target so as to correspondingly generate the power distribution strategy according to the weight values.
2. The new energy electric vehicle energy management method of claim 1, further comprising:
and establishing communication connection with an instrument panel in the vehicle currently through the CAN bus, and displaying the real-time working power of the battery module, the electricity load module and the braking energy recovery module in the instrument panel in real time.
3. A new energy electric vehicle energy management system for implementing the new energy electric vehicle energy management method according to any one of claims 1 to 2, the system comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for respectively establishing communication connection with a battery module, an electric load module and a braking energy recovery module in a vehicle through a CAN bus, and acquiring a first state parameter corresponding to the battery module, a second state parameter corresponding to the electric load module and a third state parameter corresponding to the braking energy recovery module in real time;
the training module is used for generating a corresponding monitoring data set according to the first state parameter, the second state parameter and the third state parameter, and training a preset neural network through the monitoring data set to generate a corresponding energy management model;
the processing module is used for acquiring the output torque of the driving motor and determining a corresponding optimization target and constraint conditions in the battery module, the electricity load module and the braking energy recovery module according to the output torque through the energy management model;
and the control module is used for generating a corresponding power distribution strategy according to the optimization target and the constraint condition, and controlling the actual working power of the battery module, the electricity load module and the braking energy recovery module according to the power distribution strategy.
4. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the new energy electric vehicle energy management method according to any one of claims 1 to 2 when executing the computer program.
5. A readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements the new energy electric vehicle energy management method according to any one of claims 1 to 2.
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